Recognition and prediction of driver’s whole body posture model

The driver’s whole-body posture at the time of a collision is a key factor in determining the magnitude of injury to the driver. However, current researchs on driver posture models only consider the upper body posture of the driver, and the lower body area which is not perceived by sensors is not studied. This paper investigates the driver’s posture and establishes a 3D posture model of the driver’s whole body through the application of machine vision algorithms and regression model statistics. This study proposes an improved Kinect-OpenPose algorithm for identifying the 3D spatial coordinates of nine keypoints of the driver’s upper body. The posture prediction regression model of four keypoints of the lower body is established by conducting volunteer posture acquisition experiments on the developed simulated driving seat and analyzing the volunteer posture data through using the principal components of the upper body keypoints and the seat parameters. The experiments proved that the error of the regression model in this paper is minor than that of current studies, and the accuracy of the keypoint location and the keypoint connection length of the established driver whole body posture model is high, which provides implications for future studies.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

Solution of steering angle based on homogeneous transformation

Steering Angle is related to the design and optimization of steering mechanism and suspension, but it is not equal to the angle of knuckle around kingpin because of the existence of wheel alignment parameters. To calculate the steering angle, this paper derives based on homogeneous transformation its function expression by analyzing spatial geometric relation between the two angles and calculating coordinates related to steering trajectory of wheel center. Then, multi-body model of McPherson suspension with steering system is built and the calculation correctness is verified by comparing the function curve plotted by MATLAB software with the curve simulated by Adams/Car software. The calculation and simulation indicate that between the two angles, there is a ratio which is related to wheel alignment parameters and greater than 1.

Aerodynamic instabilities modeling under asymmetric boundary in a centrifugal compressor for turbocharger

The increasing demand for compression systems with high pressure ratio and wide safety margin has set new prerequisites for designers to meet the industrial needs without increasing the manufacturing costs excessively. In this work, the turbulent stability of the vaneless diffuser of the centrifugal compressor was analyzed. Unsteady Reynolds-averaged numerical simulations of the isolated diffuser and full annular diffuser with or without circumferential asymmetric boundary conditions downstream were performed. And a continuous adjoint approach was adopted, which is rarely applied in the stability analysis of compressor flow. Then, the origin of instability under different inflow and outflow conditions was sought with a sensitivity analysis. The prediction of the growth rate reveals that the flow near the shroud dominates the global stability of the diffuser. When connected with an impeller in the upstream direction, the most unstable region is localized at the backflow regions near the outlet. The wave number, however, is altered under the impact of the jet-wake flow. When connected to a circumferential asymmetric condition, the structural sensitivity of the vaneless diffuser with a radius ratio of 1.53 indicates that the interaction between the inlet reverse flow and outlet backflow is responsible for the occurrence of stall. The most unstable regions are localized at the region 90°–135° away from the volute tongue. The present work mainly contributes to the instabilities identification with novel sensitivity methods under asymmetric boundary conditions.

Modeling and control of heat pump system for battery electric buses

Given the continuous tightening of emissions regulations on vehicles, battery-electric buses (BEB) play an essential role in the transition toward cleaner transport technologies, as they represent the most promising solution to replace diesel buses and reduce their environmental impact in the short term. However, heating the bus cabin leads to a considerable increase in energy consumption under cold weather conditions, which significantly reduces the driving range, given the limited battery capacity. Heat pumps (HP) are the primary heating technology used in BEB for their improved consumption performance compared to other technologies. Therefore, this study aims at optimizing the coefficient of performance (COP) of an HP system in a BEB for maximizing the bus electric driving range under cold weather conditions while maintaining satisfactory thermal comfort levels for passengers. Accordingly, an HP model is developed and integrated into an electric bus model using Dymola. A genetic algorithm (GA) based controller is proposed to find the optimal combination of the HP operating parameters, namely the compressor speed, the air mass flow rate at the inlet of the condenser, and the recirculation rate in order to maximize the system’s COP, and extend the BEB driving at different external temperatures, and as a function of the passengers’ occupancy levels. Results are carried under transient and steady-state operating conditions and show that the proposed GA-based controller saves up to 39% of the HP energy consumption as compared to the conventional HP control strategy, and therefore, enhances the BEB driving range up to 17%.

A novel phenomenological model for predicting hysteresis loss of rubber compounds obtained from ultra-large off-the-road tires

The objective of this study was to develop a novel phenomenological model that can predict the hysteresis loss of rubber compounds obtained from ultra-large off-the-road (OTR) tires under typical operating conditions at mine sites. To achieve this, first, cyclic tensile tests were conducted on tire tread compounds to derive the experimental results of hysteresis curves, peak stress, residual strain, and hysteresis loss at 6 strain levels, 8 strain rates, and 14 rubber temperatures. Then, referring to these experimental results, a phenomenological model was developed – the HLSRT model (a hysteresis loss model considering strain levels, strain rates, and rubber temperatures). This HLSRT model was generated based on a novel strain energy function that was modified from the traditional Mooney-Rivlin (MR) function, and the model was used to predict the hysteresis loss of rubber compounds in OTR tires. The prediction results show that the HLSRT model estimated the hysteresis loss of tire tread compounds with average and maximum mean absolute percent errors (MAPEs) of 11.2% and 18.6%, respectively, at strain levels ranging from 10% to 100%, strain rates from 10% to 500% s−1, and rubber temperatures from −30°C to 100°C. These MAPEs were relatively low when compared with previous studies, showing that the HLSRT model has higher prediction accuracy. For the first time, the HLSRT model derived from this study has provided a new approach to predicting the hysteresis loss of OTR tire rubbers to guide the use of OTR tires in truck haulage at mine sites.

A diesel engine air path model with a WG and an ETV for exhaust temperature controller design and embedded control

In this paper a mean value model of a turbocharged diesel engine air path with an electric wastegate (WG) and an exhaust throttle valve (ETV) is presented. The model is designed with regard to system analysis, controller design, and real-time feasibility. That means, care is taken to ensure that the model contains the relevant dynamics on the one hand and that the requirements for computing power and memory (RAM) are kept as low as possible on the other hand. New approaches for modeling the ETV and the exhaust gas temperature are presented. The latter is formulated via an artificial neural network (ANN) computed outside the model. The ANN is integrated into the model in such a way that the differentiation of the model still provides meaningful results for controller design. Thus, this model may also be used for online computation of nonlinear model predictive controllers (MPC) or nonlinear state observers. The parameters of the model are determined using GT-Power simulation data covering the entire working range of the engine. Only measured variables that are also accessible on the engine test bench are used. All optimization problems to be solved within the parameter determination are presented. It is analyzed which sensors are suitable to support the model in an implementation on an electronic control unit (ECU), and the effect without and with sensor correction is shown in a dynamic test bench measurement. Furthermore, the properties of the generated C code are presented, which are the number of mathematical operations, the runtimes, and the stack size. An evaluation of the real time capability is given based on eigenvalue analyses and the properties of the C code .

A SOE estimation method for lithium batteries considering available energy and recovered energy

State of energy (SOE) is a critical index of lithium battery. The problem of the inaccurate available energy and recovered energy of lithium battery affects the accuracy of SOE estimation. In order to solve the problem, this paper proposes a method to estimate the available discharge energy of lithium batteries based on response surface model. In this method, the energy efficiency of lithium batteries in different states is obtained by establishing the relationship model of external charge voltage and external discharge voltage, so as to estimate the actual available energy of lithium batteries in different charge states. On this basis, a correction method based on radial basis function (RBF) neural network is proposed to estimate the actual energy released by the recovered energy when the current direction of the battery is changed. The proposed energy correction method is combined with the adaptive particle filter algorithm to estimate SOE. This method is not limited to the assumption of Gaussian function and can accurately predict the noise variance, so as to improve the estimation accuracy of SOE. Simulations under urban dynamometer driving schedule (UDDS) are conducted, and the result shows that the proposed method can effectively estimate the battery energy and improve the accuracy of SOE estimation.

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

Front vehicle detection based on improved fusion method for lidar and visual image

In order to ensure the detection accuracy, an improved adaptive weighted (IAW) method is proposed in this paper to fuse the data of images and lidar sensors for the vehicle object’s detection. Firstly, the IAW method is proposed in this paper and the first simulation is conducted. The unification of two sensors’ time and space should be completed at first. The traditional adaptive weighted average method (AWA) will amplify the noise in the fusion process, so the data filtered with Kalman Filter (KF) algorithm instead of with the AWA method. The proposed IAW method is compared with the AWA method and the Distributed Weighted fusion KF algorithm in the data fusion simulation to verify the superiority of the proposed algorithm. Secondly, the second simulation is conducted to verify the robustness and accuracy of the IAW algorithm. In the two experimental scenarios of sparse and dense vehicles, the vehicle detection based on image and lidar is completed, respectively. The detection data is correlated and merged through the IAW method, and the results show that the IAW method can correctly associate and fuse the data of the two sensors. Finally, the real vehicle test of object vehicle detection in different environments is carried out. The IAW method, the KF algorithm, and the Distributed Weighted fusion KF algorithm are used to complete the target vehicle detection in the real vehicle, respectively. The advantages of the two sensors can give full play, and the misdetection of the target objects can be reduced with proposed method. It has great potential in the application of object acquisition.